Many optical satellite imaging systems produce two kinds of images: Panchromatic (Pan) and multi-spectral (MS). Pan images typically provide high spatial resolution but no color information, whereas MS images typically provide color spectrum information but reduced spatial resolution.
For a large number of applications, high resolution (HR) MS images are preferred. The HR MS images, which are not readily available from the satellite, can only be obtained by fusing the Pan and MS images. This fusion process is referred to as Pan-sharpening.
A number of Pan-sharpening methods are known. Those methods can be partitoned in four categories: Intensity-Hue-Saturation (IHS) transform based methods, Principal Component Analysis (PCA) based methods, arithmetic combination based methods and wavelet-based methods.
Generally, Pan-sharpened images generated by methods in the first three categories have good spatial resolution, but a distorted color spectrum. Those using wavelet-based methods exhibit relatively better color spectrum, but produce wavelet-induced artifacts.
A variety of methods to improve spatial and spectral accuracy have are known, each specific to a particular fusion technique or image set.
Compressive sensing, sparse representations and dictionary learning (DL) provide tools to address this problem. Those methods assume that the HR MS image is sparse in some basis or dictionary. The HR MS image is recovered using sparsity promoting optimization methods, subject to data fidelity constraints derived from the available LR MS and FIR Pan images. The choice of basis or dictionary is often critical in the performance of such methods. It is known that a large number of sparsity-based methods can benefit significantly from an appropriate sparsity-inducing dictionary learned from available data.